41-Issue 3

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https://diglib.eg.org/handle/10.2312/2633194

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Level of Detail Exploration of Electronic Transition Ensembles using Hierarchical Clustering
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Sidwall Thygesen, Signe; Masood, Talha Bin; Linares, Mathieu; Natarajan, Vijay; Hotz, Ingrid; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
We present a pipeline for the interactive visual analysis and exploration of molecular electronic transition ensembles. Each ensemble member is specified by a molecular configuration, the charge transfer between two molecular states, and a set of physical properties. The pipeline is targeted towards theoretical chemists, supporting them in comparing and characterizing electronic transitions by combining automatic and interactive visual analysis. A quantitative feature vector characterizing the electron charge transfer serves as the basis for hierarchical clustering as well as for the visual representations. The interface for the visual exploration consists of four components. A dendrogram provides an overview of the ensemble. It is augmented with a level of detail glyph for each cluster. A scatterplot using dimensionality reduction provides a second visualization, highlighting ensemble outliers. Parallel coordinates show the correlation with physical parameters. A spatial representation of selected ensemble members supports an in-depth inspection of transitions in a form that is familiar to chemists. All views are linked and can be used to filter and select ensemble members. The usefulness of the pipeline is shown in three different case studies.
LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Hong, Jiayi; Trubuil, Alain; Isenberg, Tobias; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.
Neural Flow Map Reconstruction
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Sahoo, Saroj; Lu, Yuzhe; Berger, Matthew; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time-varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generate in situ that are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time-varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem - learning a function-space neural network to reproduce flow map samples under a fixed integration scheme - leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.
Reusing Interactive Analysis Workflows
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Gadhave, Kiran; Cutler, Zach; Lex, Alexander; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Interactive visual analysis has many advantages, but an important disadvantage is that analysis processes and workflows cannot be easily stored and reused. This is in contrast to code-based analysis workflows, which can simply be run on updated datasets, and adapted when necessary. In this paper, we introduce methods to capture workflows in interactive visualization systems for different interactions such as selections, filters, categorizing/grouping, labeling, and aggregation. These workflows can then be applied to updated datasets, making interactive visualization sessions reusable. We demonstrate this specification using an interactive visualization system that tracks interaction provenance, and allows generating workflows from the recorded actions. The system can then be used to compare different versions of datasets and apply workflows to them. Finally, we introduce a Python library that can load workflows and apply it to updated datasets directly in a computational notebook, providing a seamless bridge between computational workflows and interactive visualization tools.
SurfNet: Learning Surface Representations via Graph Convolutional Network
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Han, Jun; Wang, Chaoli; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
For scientific visualization applications, understanding the structure of a single surface (e.g., stream surface, isosurface) and selecting representative surfaces play a crucial role. In response, we propose SurfNet, a graph-based deep learning approach for representing a surface locally at the node level and globally at the surface level. By treating surfaces as graphs, we leverage a graph convolutional network to learn node embedding on a surface. To make the learned embedding effective, we consider various pieces of information (e.g., position, normal, velocity) for network input and investigate multiple losses. Furthermore, we apply dimensionality reduction to transform the learned embeddings into 2D space for understanding and exploration. To demonstrate the effectiveness of SurfNet, we evaluate the embeddings in node clustering (node-level) and surface selection (surface-level) tasks. We compare SurfNet against state-of-the-art node embedding approaches and surface selection methods. We also demonstrate the superiority of SurfNet by comparing it against a spectral-based mesh segmentation approach. The results show that SurfNet can learn better representations at the node and surface levels with less training time and fewer training samples while generating comparable or better clustering and selection results.
VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Mahajan, Shambhavi; Chen, Bonnie; Karduni, Alireza; Kim, Yea-Seul; Wall, Emily; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
The process of forming, expressing, and updating beliefs from data plays a critical role in data-driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Recently, belief-driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief-driven visualization. In this paper, we synthesize a design space for belief-driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief-driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.
Seeing Through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Ruobin; Jung, Crescentia; Kim, Yea-Seul; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Sonification can be an effective medium for people with visual impairments to understand data in visualizations. However, there are no universal design principles that apply to various charts that encode different data types. Towards generalizable principles, we conducted an exploratory experiment to assess how different auditory channels (e.g., pitch, volume) impact the data and visualization perception among people with visual impairments. In our experiment, participants evaluated the intuitiveness and accuracy of the mapping of auditory channels on different data and chart types. We found that participants rated pitch to be the most intuitive, while the number of tappings and the length of sounds yielded the most accurate perception in decoding data. We study how audio channels can intuitively represent different charts and demonstrate that data-level perception might not directly transfer to chart-level perception as participants reflect on visual aspects of the charts while listening to audio. We conclude by how future experiments can be designed to establish a robust ranking for creating audio charts.
HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Appleby, Gabriel; Espadoto, Mateus; Chen, Rui; Goree, Samuel; Telea, Alexandru C.; Anderson, Erik W.; Chang, Remco; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Projection algorithms such as t-SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter values is computationally intensive and unintuitive due to the stochastic nature of such methods. In this paper we propose HyperNP, a scalable method that allows for real-time interactive hyperparameter exploration of projection methods by training neural network approximations. A HyperNP model can be trained on a fraction of the total data instances and hyperparameter configurations that one would like to investigate and can compute projections for new data and hyperparameters at interactive speeds. HyperNP models are compact in size and fast to compute, thus allowing them to be embedded in lightweight visualization systems. We evaluate the performance of HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP models are accurate, scalable, interactive, and appropriate for use in real-world settings.
A Flip-book of Knot Diagrams for Visualizing Surfaces in 4-Space
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Liu, Huan; Zhang, Hui; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Just as 2D shadows of 3D curves lose structure where lines cross, 3D graphics projections of smooth 4D topological surfaces are interrupted where one surface intersects itself. They twist, turn, and fold back on themselves, leaving important but hidden features behind the surface sheets. In this paper, we propose a smart slicing tool that can read the 4D surface in its entropy map and suggest the optimal way to generate cross-sectional images - or ''slices'' - of the surface to visualize its underlying 4D structure. Our visualization thinks of a 4D-embedded surface as a collection of 3D curves stacked in time, very much like a flip-book animation, where successive terms in the sequence differ at most by a critical change. This novel method can generate topologically meaningful visualization to depict complex and unfamiliar 4D surfaces, with the minimum number of cross-sectional diagrams. Our approach has been successfully used to create flip-books of diagrams to visualize a range of known 4D surfaces. In this preliminary study, our results show that the new visualization and slicing tool can help the viewers to understand and describe the complex spatial relationships and overall structures of 4D surfaces.
Mobile and Multimodal? A Comparative Evaluation of Interactive Workplaces for Visual Data Exploration
(The Eurographics Association and John Wiley & Sons Ltd., 2022) León, Gabriela Molina; Lischka, Michael; Luo, Wei; Breiter, Andreas; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Mobile devices are increasingly being used in the workplace. The combination of touch, pen, and speech interaction with mobile devices is considered particularly promising for a more natural experience. However, we do not yet know how everyday work with multimodal data visualizations on a mobile device differs from working in the standard WIMP workplace setup. To address this gap, we created a visualization system for social scientists, with a WIMP interface for desktop PCs, and a multimodal interface for tablets. The system provides visualizations to explore spatio-temporal data with consistent WIMP and multimodal interaction techniques. To investigate how the different combinations of devices and interaction modalities affect the performance and experience of domain experts in a work setting, we conducted an experiment with 16 social scientists where they carried out a series of tasks with both interfaces. Participants were significantly faster and slightly more accurate on the WIMP interface. They solved the tasks with different strategies according to the interaction modalities available. The pen was the most used and appreciated input modality. Most participants preferred the multimodal setup and could imagine using it at work. We present our findings, together with their implications for the interaction design of data visualizations.
Optimizing Grid Layouts for Level-of-Detail Exploration of Large Data Collections
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Frey, Steffen; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
This paper introduces an optimization approach for generating grid layouts from large data collections such that they are amenable to level-of-detail presentation and exploration. Classic (flat) grid layouts visually do not scale to large collections, yielding overwhelming numbers of tiny member representations. The proposed local search-based progressive optimization scheme generates hierarchical grids: leaves correspond to one grid cell and represent one member, while inner nodes cover a quadratic range of cells and convey an aggregate of contained members. The scheme is solely based on pairwise distances and jointly optimizes for homogeneity within inner nodes and across grid neighbors. The generated grids allow to present and flexibly explore the whole data collection with arbitrary local granularity. Diverse use cases featuring large data collections exemplify the application: stock market predictions from a Black-Scholes model, channel structures in soil from Markov chain Monte Carlo, and image collections with feature vectors from neural network classification models. The paper presents feedback by a domain scientist, compares against previous approaches, and demonstrates visual and computational scalability to a million members, surpassing classic grid layout techniques by orders of magnitude.
Exploring How Visualization Design and Situatedness Evoke Compassion in the Wild
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Morais, Luiz; Andrade, Nazareno; Sousa, Dandara; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
This work explores how the design and situatedness of data representations affect people's compassion with a case study concerning harassment episodes in a public place. Results contribute to advancing the understanding of how visualizations can evoke emotions and their impact on prosocial behaviors, such as helping people in need. Recent literature examined the effect of different on-screen data representations on emotion or prosociality, but little has been done concerning visualizations shown in a public place - especially a space contextually relevant to the data - or presented through unconventional media formats such as physical marks. We conducted two in-the-wild studies to investigate how different factors affect people's selfreported compassion and intention to donate. We compared three ways of presenting data about the harassment cases: (1) communicating data only verbally; (2) using a printed poster with aggregated information; and (3) using a physicalization with detailed information about each story. We found that the physicalization influenced people to donate more than only hearing about the data, but it is unclear if the same applied to the poster visualization. Also, passers-by reported a likely small increase in compassion when they saw the physicalization instead of the poster. We also examined the role of situatedness by showing the physicalization in a site that is not contextually relevant to the data. Our results suggest that people had a similar intention to donate and levels of compassion in both places. Those findings may indicate that using specific visualization designs to support campaigns about sensitive causes (e.g., sexual harassment) can increase the emotional response of passers-by and may motivate them to help, independently of where the data representation is shown. Finally, this work also informs on the strengths and weaknesses of using research in the wild to evaluate data visualizations in public spaces.
Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Lo, Leo Yu-Ho; Gupta, Ayush; Shigyo, Kento; Wu, Aoyu; Bertini, Enrico; Qu, Huamin; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Data visualization is powerful in persuading an audience. However, when it is done poorly or maliciously, a visualization may become misleading or even deceiving. Visualizations give further strength to the dissemination of misinformation on the Internet. The visualization research community has long been aware of visualizations that misinform the audience, mostly associated with the terms ''lie'' and ''deceptive.'' Still, these discussions have focused only on a handful of cases. To better understand the landscape of misleading visualizations, we open-coded over one thousand real-world visualizations that have been reported as misleading. From these examples, we discovered 74 types of issues and formed a taxonomy of misleading elements in visualizations. We found four directions that the research community can follow to widen the discussion on misleading visualizations: (1) informal fallacies in visualizations, (2) exploiting conventions and data literacy, (3) deceptive tricks in uncommon charts, and (4) understanding the designers' dilemma. This work lays the groundwork for these research directions, especially in understanding, detecting, and preventing them.
Nested Papercrafts for Anatomical and Biological Edutainment
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Schindler, Marwin; Korpitsch, Thorsten; Raidou, Renata Georgia; Wu, Hsiang-Yun; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
In this paper, we present a new workflow for the computer-aided generation of physicalizations, addressing nested configurations in anatomical and biological structures. Physicalizations are an important component of anatomical and biological education and edutainment. However, existing approaches have mainly revolved around creating data sculptures through digital fabrication. Only a few recent works proposed computer-aided pipelines for generating sculptures, such as papercrafts, with affordable and readily available materials. Papercraft generation remains a challenging topic by itself. Yet, anatomical and biological applications pose additional challenges, such as reconstruction complexity and insufficiency to account for multiple, nested structures-often present in anatomical and biological structures. Our workflow comprises the following steps: (i) define the nested configuration of the model and detect its levels, (ii) calculate the viewpoint that provides optimal, unobstructed views on inner levels, (iii) perform cuts on the outer levels to reveal the inner ones based on the viewpoint selection, (iv) estimate the stability of the cut papercraft to ensure a reliable outcome, (v) generate textures at each level, as a smart visibility mechanism that provides additional information on the inner structures, and (vi) unfold each textured mesh guaranteeing reconstruction. Our novel approach exploits the interactivity of nested papercraft models for edutainment purposes.
Exploring Multivariate Event Sequences with an Interactive Similarity Builder
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Xu, Shaobin; Sun, Minghui; Zhang, Zhengtai; Xue, Hao; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Similarity-based exploration is an effective method in knowledge discovery. Faced with multivariate event sequence data (MVES), developing a satisfactory similarity measurement for a specific question is challenging because of the heterogeneity introduced by numerous attributes with different data formats, coupled with their associations. Additionally, the absence of effective validation feedback makes judging the goodness of a measurement scheme a time-consuming and error-prone procedure. To free analysts from tedious programming to concentrate on the exploration of MVES data, this paper introduces an interactive similarity builder, where analysts can use visual building blocks for assembling similarity measurements in a drag-and-drop and incremental fashion. Based on the builder, we further propose a visual analytics framework that provides multi-granularity visual validations for measurement schemes and supports a recursive workflow for refining the focus set. We illustrate the power of our prototype through a case study and a user study with real-world datasets. Results suggest that the system improves the efficiency of developing similarity measurements and the usefulness of exploring MVES data.
Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Troidl, Jakob; Cali, Corrado; Gröller, Eduard; Pfister, Hanspeter; Hadwiger, Markus; Beyer, Johanna; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
High-resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In-depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain-specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in-depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.
Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Wu, Mengxi; Chiang, Yi-Jen; Musco, Christopher; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Key time steps selection, i.e., selecting a subset of most representative time steps, is essential for effective and efficient scientific visualization of large time-varying volume data. In particular, as computer simulations continue to grow in size and complexity, they often generate output that exceeds both the available storage capacity and bandwidth for transferring results to storage, making it indispensable to save only a subset of time steps. At the same time, this subset must be chosen so that it is highly representative, to facilitate post-processing and reconstruction with high fidelity. The key time steps selection problem is especially challenging in the in situ setting, where we can only process data in one pass in an online streaming fashion, using a small amount of main memory and fast computation. In this paper, we formulate the problem as that of optimal piece-wise linear interpolation. We first apply a method from numerical linear algebra to compute linear interpolation solutions and their errors in an online streaming fashion. Using that method as a building block, we can obtain a global optimal solution for the piece-wise linear interpolation problem via a standard dynamic programming (DP) algorithm. However, this approach needs to process the time steps in multiple passes and is too slow for the in situ setting. To address this issue, we introduce a novel approximation algorithm, which processes time steps in one pass in an online streaming fashion, with very efficient computing time and main memory space both in theory and in practice. The algorithm is suitable for the in situ setting. Moreover, we prove that our algorithm, which is based on a greedy update rule, has strong theoretical guarantees on the approximation quality and the number of time steps stored. To the best of our knowledge, this is the first algorithm suitable for in situ key time steps selection with such theoretical guarantees, and is the main contribution of this paper. Experiments demonstrate the efficacy of our new techniques.
Urban Rhapsody: Large-scale Exploration of Urban Soundscapes
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Rulff, João; Miranda, Fabio; Hosseini, Maryam; Lage, Marcos; Cartwright, Mark; Dove, Graham; Bello, Juan; Silva, Claudio T.; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Noise is one of the primary quality-of-life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low-cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes. In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state-of-the-art audio representation, machine learning and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high-precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five-year deployment of a one-of-a-kind sensor network in New York City.
AirLens: Multi-Level Visual Exploration of Air Quality Evolution in Urban Agglomerations
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Qu, Dezhan; Lv, Cheng; Lin, Yiming; Zhang, Huijie; Wang, Rong; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi-run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi-level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.
Visual Parameter Selection for Spatial Blind Source Separation
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Piccolotto, Nikolaus; Bögl, Markus; Muehlmann, Christoph; Nordhausen, Klaus; Filzmoser, Peter; Miksch, Silvia; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non-expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data.

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